黄伟Tensorflow笔记一

ゝ一世哀愁。 2023-05-30 09:19 16阅读 0赞

# Tensorflow笔记一 #

## 一. 目的： ##

生成干扰数据 计算 Y = 2 * X   的结果

## 二.代码 ##

# 完整代码
    # 计算 Y = 2 * X    的值
    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    
    def moving_average(a,w=10):
        if(len(a)<w):
            return a[:]
        return [val if idx<w else sum(a[(idx-w):idx])/w for idx,val in enumerate(a)]
    
    
    
    # 生成100 个 -1 到 1 的数据
    trainX = np.linspace(-1,1,100)
    
    # 生成100个干扰数据
    trainY = 2 * trainX + np.random.randn(*trainX.shape) * 0.3
    
    # 绘图
    plt.plot(trainX,trainY,'ro',label = 'I Love China')
    plt.legend()
    plt.show()
    
    # 占位
    X = tf.placeholder("float")
    Y = tf.placeholder("float")
    
    # 模型参数
    W = tf.Variable(tf.random_normal([1]), name = 'bias')
    B = tf.Variable(tf.zeros([1]),name="bias")
    
    
    # 前向结构   前向传播
    Z = tf.multiply(X,W) + B
    
    
    #  反向优化
    cost = tf.reduce_mean(tf.square(Y - Z))
    learning_rate = 0.01
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
    
    
    # 迭代训练模型
    init = tf.global_variables_initializer()
    
    # 定义参数
    training_epochs = 20
    display_step = 2
    
    #  启动session
    with tf.Session() as sess:
        sess.run(init)
        plotdata = {"batchsize":[],"loss":[]}
        
        # 向模型输入数据
        for epoch in range(training_epochs):
            for(x,y) in zip(trainX,trainY):
               sess.run(optimizer,feed_dict={X:x,Y:y})
            
            # 显示训练中的详细信息
            if epoch % display_step == 0:
                loss = sess.run(cost,feed_dict={X:trainX,Y: trainY})
                print("Epoch:",epoch + 1,"cost = ",loss,"W=",sess.run(W),"b=",sess.run(B))
                if not (loss == "NA"):
                    plotdata["batchsize"].append(epoch)
                    plotdata["loss"].append(loss)
                    
                    
                    
                    
        print("Finished")
        print("cost=",sess.run(cost,feed_dict={X:trainX,Y: trainY}),"W=",sess.run(W),"b=",sess.run(B))
        
        # 图像显示  
        plt.plot(trainX,trainY,'ro',label = 'sad data')
        plt.plot(trainX,sess.run(W)*trainX + sess.run(B),label='Fittedline')
        plt.legend()
        plt.show()
        
        
        plotdata["avgloss"] = moving_average(plotdata["loss"])
        plt.figure(1)
        plt.subplot(211)
        plt.plot(plotdata["batchsize"],plotdata["avgloss"],'b--')
        plt.xlabel('minibatch number')
        plt.ylabel('Loss')
        plt.title('Minibatch run vs. Training loss')
        plt.show()
        
        print("x=0.2,z=",sess.run(Z,feed_dict={X:0.2}))

## 三、运行结果 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2h1YW5nX3dlX2k_size_16_color_FFFFFF_t_70]

Epoch: 1 cost = 0.19036867 W= \[1.5034635\] b= \[0.15345636\]  
Epoch: 3 cost = 0.09338717 W= \[1.8725581\] b= \[0.05529383\]  
Epoch: 5 cost = 0.08592511 W= \[1.9727246\] b= \[0.01758706\]  
Epoch: 7 cost = 0.085566826 W= \[1.9987036\] b= \[0.00763\]  
Epoch: 9 cost = 0.08558113 W= \[2.0054226\] b= \[0.00505186\]  
Epoch: 11 cost = 0.08559199 W= \[2.00716\] b= \[0.00438513\]  
Epoch: 13 cost = 0.08559528 W= \[2.00761\] b= \[0.00421249\]  
Epoch: 15 cost = 0.085596174 W= \[2.0077255\] b= \[0.00416822\]  
Epoch: 17 cost = 0.08559638 W= \[2.0077548\] b= \[0.00415687\]  
Epoch: 19 cost = 0.08559645 W= \[2.0077631\] b= \[0.00415374\]  
Finished  
cost= 0.08559646 W= \[2.0077643\] b= \[0.00415323\]

x=0.2,z= \[0.4057061\]  
![在这里插入图片描述][20191109103404159.png]  
x=0.2,z= \[0.4057061\]

## 四、小结 ##

#### （1）linspace ####

# 作用：生成100 个 -1 到 1的数 
    # start 参数1： 起始位置
    # stop 参数2： 结束位置
    # num 参数3： 生成个数，默认50
    # endpoint 参数4 ：是否包含 stop 数值，默认为True，包含stop值；若为False，则不包含stop值
    # retstep 参数5: 默认为False，返回等差数列数组[-1. -0.5 0. 0.5 1. ]，
    # 若为True，则返回结果(array([`samples`, `step`]))
    # return 返回： num 个 start 到 stop 之间数组成的数组或列表
    
    trainX = np.linspace(-1,1,100)

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2h1YW5nX3dlX2k_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20191109103345916.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2h1YW5nX3dlX2k=,size_16,color_FFFFFF,t_70
[20191109103404159.png]: https://img-blog.csdnimg.cn/20191109103404159.png